R Notebook

setwd("C:/Users/iris9/BioTrajX")

Warning: The working directory was changed to C:/Users/iris9/BioTrajX inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

devtools::load_all() 

ℹ Loading BioTrajX

library(Seurat)
seu <- readRDS("C:/Users/iris9/Desktop/GSE164690.all.CD8posT.wScores_pseudotime.rds")
expr <- as.matrix(GetAssayData(seu, layer = "data"))
t<- seu$monocle3_pseudotime
naive<- c("CCR7","TCF7","IL7R")
term<- c('PDCD1','LAG3','HAVCR2','TOX','TIGIT','CTLA4','CXCL13','ENTPD1','ITGAE')
cluster <- seu$functional.cluster

DimPlot(seu, group.by = "functional.cluster")

FeaturePlot(seu, features="monocle3_pseudotime")

FeaturePlot(seu, features="slingshot_pseudotime")

FeaturePlot(seu, features="cytotrace_score")

D <- metrics_D(expr, naive_markers = naive, terminal_markers = term, seu$monocle3_pseudotime)


plot_metrics_D(seu$monocle3_pseudotime,D)

O <- metrics_O(expr, naive_markers = naive, terminal_markers = term, seu$monocle3_pseudotime)
plot_metrics_O(expr,pseudotime = seu$monocle3_pseudotime, O ,naive_markers = naive, terminal_markers = term)

P <- metrics_P(expr,
              seu$monocle3_pseudotime,
                      pathways = c("hsa04660","hsa04640"),
                      naive_markers = naive,
                      terminal_markers = term,
                      id_type = c("SYMBOL","ENSEMBL","ENTREZID"),
                      species = "Homo sapiens",
                      assay = NULL,
                      slot  = "data",
                      min_remaining = 10,
                      min_fraction  = 0.20,
                      min_genes_per_module = 3,
                      verbose = TRUE)

plot_metrics_P(expr,seu$monocle3_pseudotime, P)

E <- metrics_E(seu$monocle3_pseudotime,
              naive_marker_scores = D$s_i_naive,
              terminal_marker_scores = D$s_i_term,
              method = c("gmm"))

plot_metrics_E(E)            

compute_DOPE_single(seu,
                    seu$cytotrace_score,
                    naive_markers = naive,
                    terminal_markers = term, 
                    pathways = c("hsa04660","hsa04640"))

compute_DOPE_single(seu,
                    seu$monocle3_pseudotime,
                    naive_markers = naive,
                    terminal_markers = term, 
                    pathways = c("hsa04660","hsa04640"))

res_single <- compute_single_DOPE_linear(seu,
                    seu$slingshot_pseudotime,
                    naive_markers = naive,
                    terminal_markers = term, 
                    pathways = c("hsa04660","hsa04640"))

pseudotime_methods <- list(
  "monocle3" = seu$monocle3_pseudotime,
  "slingshot" = seu$slingshot_pseudotime, 
  "CytoTRACE" = seu$cytotrace_score
)

res<- compute_multi_DOPE_linear(expr,
                          pseudotime_list = pseudotime_methods,
                          naive_markers = naive,
                          terminal_markers =term,
                          pathways = c("hsa04660","hsa04640"))

Computing DOPE metrics for 3 trajectories...
==========================================================

--- Processing trajectory: monocle3 ---

--- Processing trajectory: slingshot ---

--- Processing trajectory: CytoTRACE ---

==========================================================
Multi-trajectory DOPE analysis complete!
Best trajectory: slingshot (DOPE score: 0.687)

plot.multi_dope_results(res, type = "bar")